1. Reliable and Efficient Routing Protocols for Vehicular Communication Networks Katsaros Konstantinos PhD Student Supervisor: Dr. M. Dianati Co-supervisor: Prof. R. Tafazolli Transfer Presentation

2. Outline  Introduction  Scope, Objectives, Challenges  Routing in VANETs  Taxonomy, Forwarding techniques, Recovery strategies, Cross-layering  Achievements so far  Proposed CLWPR (System model, design characteristics)  Performance evaluation  Future plan Konstantinos Katsaros 2

3. Scope Intelligent Transportation Systems (ITS) –Application of Information and Communication Technologies for future transport systems –In order to: Improve safety and traffic management Provide infotainment services. Vehicular Communications is an important part of ITS. –Cellular (3G, LTE) and Dedicated Short Range Communications (IEEE 802.11p / WAVE) Konstantinos Katsaros 3

4. VANETs: Challenges & Opportunities Are a category of Mobile Ad-hoc Networks (MANETs) with specific characteristics: –Less strict energy and computational constraints –Highly dynamic –Predictable mobility patterns –High density of nodes Konstantinos Katsaros 4

5. Objectives of this work To design reliable and efficient routing protocols by exploiting: –Position and mobility information in order to increase efficiency –PHY and MAC information in order to increase reliability To design a Location Service –that can provide position information for the routing protocols Konstantinos Katsaros 5

6. BACKGROUND Overview of routing and forwarding protocols for MANETs and VANETs Konstantinos Katsaros 6

7. Routing Taxonomy AdvantagesDisadvantages Routing Protocols for VANETs Topology Based Proactive Do not flood entire network Fast path selection Overhead to maintain tables Reactive Do not maintain routing tables Initial delay for route discovery Flood a route request Hybrid Combination of proactive and reactive in different operation stages Hierarchical Exploit clusters with similar characteristics Overhead to maintain clusters Flooding Low complexity, high data reception Flood entire network Position Based Without Navigation Rely on local information only Need a location service (LS), more prone to local maximum problem With Navigation Exploit mobility of nodes, less prone to local maximum Need a LS, increased overhead due to enhanced beaconing Konstantinos Katsaros 7

8. Position-based Forwarding without Navigation Konstantinos Katsaros 8 S 3 5 1 2 D 4 6 7 8 Greedy Forwarding Most Forward in Radius Nearest Forwarding Progress Compass Random Positive Progress

9. Local Maximum Problem & Recovery Techniques Konstantinos Katsaros 9 S D Recovery strategies:  Drop packet  Enhanced Greedy (random retransmission once)  Carry-n-Forward  Coloring  Left hand rule  Perimeter routing

10. Position-based Forwarding with Navigation Konstantinos Katsaros 10 1.“Anchor” points at junctions with coordinator nodes 2.Enhanced beacon messages with velocity/heading 3.Position prediction policy (dead reckoning) 4.Estimation of link lifetime 5.Vehicle traffic information (max velocity, traffic density) Recovery From Local Maximum Re-route using different anchor points (with or without deletion)

11. Cross-Layer Optimization of Routing Protocols Network layer with PHY and MAC: Use channel/link quality information for routing decision Network layer with Transport and Application: Provide different levels of priorities on packets Konstantinos Katsaros 11

12. CROSS-LAYER POSITION BASED ROUTING (CLWPR) Proposed routing protocol: system model and design characteristics Konstantinos Katsaros 12

13. System Model Important Assumptions: –Position and navigation information are available (e.g., using GPS) –Nodes are equipped with the IEEE 802.11p based communication facility Konstantinos Katsaros 13

14. Main Features of CLWPR Unicast, multi-hop, cross-layer, opportunistic routing Neighbor discovery based on periodic 1-hop “HELLO” messages –“HELLO” message content: position, velocity, heading, road id, node utilization, MAC information, number of cached packets  total size 52bytes Use of position prediction and “curvemetric” distance Use of SNIR information from “HELLO” messages Employ carry-n-forward strategy for local-maximum Combine metrics in a weighting function used for forwarding decisions Konstantinos Katsaros 14

15. Weighting Function for Next Hop Selection Konstantinos Katsaros 15

16. PERFORMANCE EVALUATION Simulation setup, initial results, performance analysis and comparison Konstantinos Katsaros 16

17. Simulations Setup Performance metrics –Packet Deliver Ratio (PDR), –End-to-End Delay, –network overhead. Use ns-3 for simulations 5x5 grid network, 200 and 100 vehicles scenarios 10 concurrent vehicle-to-vehicle connections UDP packets (512 Bytes) with 2 sec interval IEEE 802.11p, 3Mbps, RTS/CTS enabled Two-Ray-Ground model Konstantinos Katsaros 17

18. Comparison with GPSR Konstantinos Katsaros 18 Increased PDR Reduced end-to-end delay —Increased overhead due to larger HELLO messages

19. Impact of HELLO interval and prediction Konstantinos Katsaros 19 Prediction improves PDR More frequent HELLO increases PDR Network overhead could be reduced by increasing HELLO interval for the same PDR threshold.

20. Influence of navigation Konstantinos Katsaros 20 Navigation improves PDR Increasing weight of navigation information has positive effect in higher vehicle speeds

21. Influence of SNIR Konstantinos Katsaros 21 SNIR information reduces end-to-end delay —Due to propagation model used, not big improvements Expect more when shadowing is included

22. Influence of Carry-n-Forward Konstantinos Katsaros 22 Increased PDR with time of caching —Increased end-to-end delay with time of caching

23. FUTURE WORK CWPR optimization, proposed location service, impact assessment and security issues Konstantinos Katsaros 23

24. Future Work (1) CLWPR Optimization –Use realistic propagation model –Optimize all weighting parameters Location Service (a) –RSUs as distributed database –Co-operation between nodes Reduce number and latency of queries Konstantinos Katsaros 24

25. Future Work (2) Location Service (b) – heterogeneous network –Use of UMTS technologies for control and signaling to provide location service Impact Assessment –Asses impact of ITS applications on network reliability Security Issues –Analyze potential threats on reliability of vehicular networks, specially for Location services Konstantinos Katsaros 25

26. Work Plan Konstantinos Katsaros 26

27. Publications Current: –K. Katsaros, et al. “CLWPR - A novel cross-layer optimized position based routing protocol for VANETs", in IEEE Vehicular Networking Conference, pp. 200-207, 2011 –K. Katsaros, et al. “Application of Vehicular Communications for Improving the Efficiency of Traffic in Urban Areas", accepted in Wireless Communications and Mobile Computing, 2011. –K. Katsaros, et al. ”Performance Analysis of a Green Light Optimized Speed Advisory (GLOSA) application using an integrated cooperative ITS simulation platform", in Proceedings of IEEE International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 918 - 923, 2011 Planned: –Survey Paper on routing protocols for VANETs –Conf. paper @ NS-3 Workshop in SIMUTools 2012, regarding the architecture and implementation (Nov. ‘11) –Journal article @ JSAC on Vehicular Communications extending CLWPR paper (Feb. ‘12) Konstantinos Katsaros 27

28. QUESTIONS Konstantinos Katsaros 28 Email: K.Katsaros@surrey.ac.ukK.Katsaros@surrey.ac.uk www: info.ee.surrey.ac.uk/Personal/K.Katsaros/info.ee.surrey.ac.uk/Personal/K.Katsaros/

29. Current work Propagation Loss Model for urban environment, initial results Konstantinos Katsaros 29

30. Winner B1 model for urban V2V Konstantinos Katsaros 30 [1] IST-WINNER D1.1.2 P. Kyösti, et al., "WINNER II Channel Models", September 2007. Available at: https://www.ist-winner.org/WINNER2-Deliverables/D1.1.2v1.1.pdf Use propagation models from [1] taking into account buildings and shadowing with LOS and NLOS components

31. TwoRayGround Vs. Winner in network graph / connections Konstantinos Katsaros 31

32. TwoRayGround Vs. Winner in PDR Konstantinos Katsaros 32

33. Cross-Layer Designs (1) Network layer with PHY and MAC: Use channel/link quality information for routing decision –Link Residual Time –SNR info for MuiltiPoint Relay selection –MAC layer position information for prediction –MAC retransmissions –DeReHQ [1]: Delay, Reliability and Hop count –PROMPT [2]: Delay aware routing and robust MAC –MAC collaboration for heterogeneous networks [1] Z. Niu, W. Yao, Q. Ni, and Y. Song, “Study on QoS Support in 802.11e-based Multi-hop Vehicular Wireless Ad Hoc Networks,” in IEEE International Conference on Networking, Sensing and Control, pp. 705 –710, 2007. [2] B. Jarupan and E. Ekici, “PROMPT: A cross-layer position-based communication protocol for delay- aware vehicular access networks,” Ad Hoc Networks, vol. 8, pp. 489–505, July 2010. Konstantinos Katsaros 33

34. Cross-Layer Designs (2) Network layer with transport and Application: Provide different levels of priorities on packets –VTP (Vehicular Transport Protocol) –Optimization of TCP and GPSR with vehicle mobility (adaptive beacon interval) Network layer with multiple layers –Joint MAC, Network and Transport [1] Konstantinos Katsaros 34 [1] L. Zhou, B. Zheng, B. Geller, a. Wei, S. Xu, and Y. Li, “Cross-layer rate control, medium access control and routing design in cooperative VANET”, Computer Communications, vol. 31, pp. 2870– 2882, July 2008

35. Location Services Flooding based: All nodes host it –Proactive: DREAM –Reactive: LAR, MALM (mobility assisted) Rendezvous based: Some nodes host it –Quorum: divide node set into two subsets (update and query) –Hashing (according to node ID or location): define server nodes using a hash function –RLSMP (Region-based Location Service Management Protocol) and MG-LSM (Mobile Group Location Service Management) designed for VANETs utilizing mobility information Konstantinos Katsaros 35